Psychological Methods: Wiki - Revision history

Sje025: /* Learning and exploring statistics */

2024-09-03T13:45:52Z

Learning and exploring statistics

← Older revision		Revision as of 13:45, 3 September 2024
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	==Learning and exploring statistics==		==Learning and exploring statistics==
	A lot of statistics and what makes learning it and dealing with it so difficult is that it is based on quite abstract assumptions. For example, it is often difficult to grasp that distributions that statistical tests are based upon (e.g., t, F or χ² (chi-square) distributions) are a mathematical description (i.e., a formula) that is based upon empirical distributions (e.g., having an incredibly large number of trials with tossing a coin or throwing a dice). [https://garthtarr.com/about/ Dr. Garth Tarr] developed an [http://statstar.io/ ~~incredibly~~ helpful web page] where students can play around with, e.g., how the mean and the standard deviation a sample of N=20 could look like if an experiment in this group is repeated again and again or how likely outliers are and how this can influence whether a statistical test becomes significant or not.		A lot of statistics and what makes learning it and dealing with it so difficult is that it is based on quite abstract assumptions. For example, it is often difficult to grasp that distributions that statistical tests are based upon (e.g., t, F or χ² (chi-square) distributions) are a mathematical description (i.e., a formula) that is based upon empirical distributions (e.g., having an incredibly large number of trials with tossing a coin or throwing a dice). [https://garthtarr.com/about/ Dr. Garth Tarr] developed a [http://statstar.io/ helpful web page] where students can play around with, e.g., how the mean and the standard deviation a sample of N=20 could look like if an experiment in this group is repeated again and again or how likely outliers are and how this can influence whether a statistical test becomes significant or not.

	==Organizing and storing your data==		==Organizing and storing your data==

Sje025: /* Quantitative data analyses */

2023-02-07T11:18:24Z

Quantitative data analyses

← Older revision		Revision as of 11:18, 7 February 2023
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	For a couple of years ago, we have changed from SPSS to jamovi for teaching. [https://www.jamovi.org/ jamovi] is a statistics package that is based upon R and quite similar in functionality to SPSS. For further information, please consult the [https://jamovi.readthedocs.io jamovi-documentation] which is available in several languages such as [https://jamovi.readthedocs.io/en/latest/ English], [https://jamovi.readthedocs.io/no/latest/ Norwegian], and [https://jamovi.readthedocs.io/de/latest/ German].<br>		For a couple of years ago, we have changed from SPSS to jamovi for teaching. [https://www.jamovi.org/ jamovi] is a statistics package that is based upon R and quite similar in functionality to SPSS. For further information, please consult the [https://jamovi.readthedocs.io jamovi-documentation] which is available in several languages such as [https://jamovi.readthedocs.io/en/latest/ English], [https://jamovi.readthedocs.io/no/latest/ Norwegian], and [https://jamovi.readthedocs.io/de/latest/ German].<br>

	It is not only that jamovi implements its analyses as R-code under the hood, it also offers plenty of opportunity to combine the “safer” option of assembling analyses using a graphical user interface (for standard analyses) with the much broader range of analyses available in R. The [[Media:JamoviToR.pdf\| following lecture]] first gives a brief introduction into R (what is it about, variable types, etc.) before demonstrating how to use R from within jamovi (using the [Rj editor]) and how to use jamovi within R (jamovi can output the syntax for the analyses conducted and all analyses are available as [https://cran.r-project.org/package=jmv jmv] R-package, and the R-packages [https://cran.r-project.org/package=jmvconnect jmvconnect] and [https://cran.r-project.org/package=jmvReadWrite jmvReadWrite] help with handling datasets opened in jamovi and jamovi-files). The lecture ends with presenting two typical use cases: using analysis results (e.g., regression coefficients) and importing text data.		It is not only that jamovi implements its analyses as R-code under the hood, it also offers plenty of opportunity to combine the “safer” option of assembling analyses using a graphical user interface (for standard analyses) with the much broader range of analyses available in R. The [[Media:JamoviToR.pdf\| following lecture]] first gives a brief introduction into R (what is it about, variable types, etc.) before demonstrating how to use R from within jamovi (using the [https://jamovi.readthedocs.io/en/latest/usermanual/um_6_jamovi_and_R.html#rj-editor Rj editor]) and how to use jamovi within R (jamovi can output the syntax for the analyses conducted and all analyses are available as [https://cran.r-project.org/package=jmv jmv] R-package, and the R-packages [https://cran.r-project.org/package=jmvconnect jmvconnect] and [https://cran.r-project.org/package=jmvReadWrite jmvReadWrite] help with handling datasets opened in jamovi and jamovi-files). The lecture ends with presenting two typical use cases: using analysis results (e.g., regression coefficients) and importing text data.

	===Qualitative or mixed-method analyses===		===Qualitative or mixed-method analyses===

Sje025: /* Quantitative data analyses */

2023-02-07T11:17:16Z

Quantitative data analyses

← Older revision		Revision as of 11:17, 7 February 2023
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	All analyses described above are used to explore manifest variables, i.e. variables that we can directly observe (and analyse). However, there are also [https://en.wikipedia.org/wiki/Latent_and_observable_variables latent variables] that are inferred indirectly through a mathematical model from observable variables. A typical example for such latent variables are personality dimensions, where we measure characteristics of that personality dimension with several items and afterwards combine them (e.g., by summing them up). A method to find or extract such dimensions based upon the pattern of correlations among questionnaire items is called (exploratory) factor analysis. The [[Media:FactorAnalysis_jamovi_Lecture.pdf\|following lecture]] first describes the principles and inner workings of factor analysis (FA) before turning to how different factor-analytical methods (exploratory FA, principal component analysis, confirmatory FA and reliability analysis) are carried out in jamovi.<br>		All analyses described above are used to explore manifest variables, i.e. variables that we can directly observe (and analyse). However, there are also [https://en.wikipedia.org/wiki/Latent_and_observable_variables latent variables] that are inferred indirectly through a mathematical model from observable variables. A typical example for such latent variables are personality dimensions, where we measure characteristics of that personality dimension with several items and afterwards combine them (e.g., by summing them up). A method to find or extract such dimensions based upon the pattern of correlations among questionnaire items is called (exploratory) factor analysis. The [[Media:FactorAnalysis_jamovi_Lecture.pdf\|following lecture]] first describes the principles and inner workings of factor analysis (FA) before turning to how different factor-analytical methods (exploratory FA, principal component analysis, confirmatory FA and reliability analysis) are carried out in jamovi.<br>

	For a couple of years ago, we have changed from SPSS to jamovi for teaching. [https://www.jamovi.org/ jamovi] is a statistics package that is based upon R and quite similar in functionality to SPSS. For further information, please consult the [https://jamovi.readthedocs.io jamovi-documentation] which is available in several languages such as [https://jamovi.readthedocs.io/en/latest/ English], [https://jamovi.readthedocs.io/de/latest/ ~~German~~], and [https://jamovi.readthedocs.io/no/latest/ ~~Norwegian~~].<br>		For a couple of years ago, we have changed from SPSS to jamovi for teaching. [https://www.jamovi.org/ jamovi] is a statistics package that is based upon R and quite similar in functionality to SPSS. For further information, please consult the [https://jamovi.readthedocs.io jamovi-documentation] which is available in several languages such as [https://jamovi.readthedocs.io/en/latest/ English], [https://jamovi.readthedocs.io/no/latest/ Norwegian], and [https://jamovi.readthedocs.io/de/latest/ German].<br>

	It is not only that jamovi implements its analyses as R-code under the hood, it also offers plenty of opportunity to combine the “safer” option of assembling analyses using a graphical user interface (for standard analyses) with the much broader range of analyses available in R. The [[Media:JamoviToR.pdf\| following lecture]] first gives a brief introduction into R (what is it about, variable types, etc.) before demonstrating how to use R from within jamovi (using the [Rj editor]) and how to use jamovi within R (jamovi can output the syntax for the analyses conducted and all analyses are available as [https://cran.r-project.org/package=jmv jmv] R-package, and the R-packages [https://cran.r-project.org/package=jmvconnect jmvconnect] and [https://cran.r-project.org/package=jmvReadWrite jmvReadWrite] help with handling datasets opened in jamovi and jamovi-files). The lecture ends with presenting two typical use cases: using analysis results (e.g., regression coefficients) and importing text data.		It is not only that jamovi implements its analyses as R-code under the hood, it also offers plenty of opportunity to combine the “safer” option of assembling analyses using a graphical user interface (for standard analyses) with the much broader range of analyses available in R. The [[Media:JamoviToR.pdf\| following lecture]] first gives a brief introduction into R (what is it about, variable types, etc.) before demonstrating how to use R from within jamovi (using the [Rj editor]) and how to use jamovi within R (jamovi can output the syntax for the analyses conducted and all analyses are available as [https://cran.r-project.org/package=jmv jmv] R-package, and the R-packages [https://cran.r-project.org/package=jmvconnect jmvconnect] and [https://cran.r-project.org/package=jmvReadWrite jmvReadWrite] help with handling datasets opened in jamovi and jamovi-files). The lecture ends with presenting two typical use cases: using analysis results (e.g., regression coefficients) and importing text data.

Sje025: /* Quantitative data analyses */

2023-02-07T11:05:24Z

Quantitative data analyses

← Older revision		Revision as of 11:05, 7 February 2023
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	===Quantitative data analyses===		===Quantitative data analyses===

	~~TBA: an introduction into general statistical~~ concepts		Statistical analyses are based upon a number of concepts and underlying assumptions. The [[Media:CrashCourseRefresher_Concepts.pdf\|following lecture]] aims to give an introduction into such general statistical concepts, discussing, e.g., population and sample, levels of measurement, how to organize and first explore your data (using descriptive statistics), and prerequisites for conducting inference statistics such as a description of what research vs. statistical hypotheses are, several conceptualizations of the p-value and a discussion of statistical significance vs. effect size.
	[[Media:CrashCourseRefresher_Concepts.pdf]]

	When choosing your evaluation method, a key criterion is whether your variables (predictor/independent and outcome/dependent) are categorical or continuous. Most analysis methods are parametric statistics (i.e., they rely on the assumption that the data are drawn from a distribution, e.g., a standard normal distribution) and based upon the [https://en.wikipedia.org/wiki/General_linear_model General linear model].		When choosing your evaluation method, a key criterion is whether your variables (predictor/independent and outcome/dependent) are categorical or continuous. Most analysis methods are parametric statistics (i.e., they rely on the assumption that the data are drawn from a distribution, e.g., a standard normal distribution) and based upon the [https://en.wikipedia.org/wiki/General_linear_model General linear model].

Sje025: /* Quantitative data analyses */

2023-02-07T10:51:38Z

Quantitative data analyses

← Older revision		Revision as of 10:51, 7 February 2023
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	[[Correlation and regression analysis]] can be used to explore the relationship between continuous predictor and continuous outcome variables. Logistic regression, where the relationship between continuous predictor and categorical outcome variables is explored, is a special case of the regression.<br>		[[Correlation and regression analysis]] can be used to explore the relationship between continuous predictor and continuous outcome variables. Logistic regression, where the relationship between continuous predictor and categorical outcome variables is explored, is a special case of the regression.<br>
	To explore the relationship between categorical predictor and continuous outcome variables, we use [[t-test and Analysis of Variance (ANOVA)]]. It is (in an ANCOVA) also possible to include continuous predictor variables, however the main focus in those analyses is typically on the categorical predictors as those represent the experimentally manipulated variables (e.g., treatment vs. control group).<br>		To explore the relationship between categorical predictor and continuous outcome variables, we use [[t-test and Analysis of Variance (ANOVA)]]. It is (in an ANCOVA) also possible to include continuous predictor variables, however the main focus in those analyses is typically on the categorical predictors as those represent the experimentally manipulated variables (e.g., treatment vs. control group).<br>
	To explore the relationship between categorical predictor and categorical outcome variables, we use frequency analyses. Those are covered as the ~~first part of~~ [[Media:CrashCourseRefresher_jamovi_Analyses.pdf\|the following lecture]].<br>		To explore the relationship between categorical predictor and categorical outcome variables, we use frequency analyses. Those are covered as the [[Media:CrashCourseRefresher_jamovi_Analyses.pdf\|first part of the following lecture]].<br>

	All analyses described above are used to explore manifest variables, i.e. variables that we can directly observe (and analyse). However, there are also [https://en.wikipedia.org/wiki/Latent_and_observable_variables latent variables] that are inferred indirectly through a mathematical model from observable variables. A typical example for such latent variables are personality dimensions, where we measure characteristics of that personality dimension with several items and afterwards combine them (e.g., by summing them up). A method to find or extract such dimensions based upon the pattern of correlations among questionnaire items is called (exploratory) factor analysis. The [[Media:FactorAnalysis_jamovi_Lecture.pdf\|following lecture]] first describes the principles and inner workings of factor analysis (FA) before turning to how different factor-analytical methods (exploratory FA, principal component analysis, confirmatory FA and reliability analysis) are carried out in jamovi.<br>		All analyses described above are used to explore manifest variables, i.e. variables that we can directly observe (and analyse). However, there are also [https://en.wikipedia.org/wiki/Latent_and_observable_variables latent variables] that are inferred indirectly through a mathematical model from observable variables. A typical example for such latent variables are personality dimensions, where we measure characteristics of that personality dimension with several items and afterwards combine them (e.g., by summing them up). A method to find or extract such dimensions based upon the pattern of correlations among questionnaire items is called (exploratory) factor analysis. The [[Media:FactorAnalysis_jamovi_Lecture.pdf\|following lecture]] first describes the principles and inner workings of factor analysis (FA) before turning to how different factor-analytical methods (exploratory FA, principal component analysis, confirmatory FA and reliability analysis) are carried out in jamovi.<br>

Sje025: /* Evaluation methods */

2023-02-07T10:48:01Z

Evaluation methods

← Older revision		Revision as of 10:48, 7 February 2023
Line 68:		Line 68:

	When choosing your evaluation method, a key criterion is whether your variables (predictor/independent and outcome/dependent) are categorical or continuous. Most analysis methods are parametric statistics (i.e., they rely on the assumption that the data are drawn from a distribution, e.g., a standard normal distribution) and based upon the [https://en.wikipedia.org/wiki/General_linear_model General linear model].		When choosing your evaluation method, a key criterion is whether your variables (predictor/independent and outcome/dependent) are categorical or continuous. Most analysis methods are parametric statistics (i.e., they rely on the assumption that the data are drawn from a distribution, e.g., a standard normal distribution) and based upon the [https://en.wikipedia.org/wiki/General_linear_model General linear model].

	~~TBA: Uni- and bivariate analyses~~
	~~[[Media:CrashCourseRefresher_jamovi_Analyses.pdf]]~~
	~~To explore the relationship between categorical predictor and categorical outcome variables, we use frequency analyses.~~

	[[Correlation and regression analysis]] can be used to explore the relationship between continuous predictor and continuous outcome variables. Logistic regression, where the relationship between continuous predictor and categorical outcome variables is explored, is a special case of the regression.<br>		[[Correlation and regression analysis]] can be used to explore the relationship between continuous predictor and continuous outcome variables. Logistic regression, where the relationship between continuous predictor and categorical outcome variables is explored, is a special case of the regression.<br>
	To explore the relationship between categorical predictor and continuous outcome variables, we use [[t-test and Analysis of Variance (ANOVA)]]. It is (in an ANCOVA) also possible to include continuous predictor variables, however the main focus in those analyses is typically on the categorical predictors as those represent the experimentally manipulated variables (e.g., treatment vs. control group).<br>		To explore the relationship between categorical predictor and continuous outcome variables, we use [[t-test and Analysis of Variance (ANOVA)]]. It is (in an ANCOVA) also possible to include continuous predictor variables, however the main focus in those analyses is typically on the categorical predictors as those represent the experimentally manipulated variables (e.g., treatment vs. control group).<br>
	~~TBA~~: ~~Factor~~ analysis		To explore the relationship between categorical predictor and categorical outcome variables, we use frequency analyses. Those are covered as the first part of [[Media:CrashCourseRefresher_jamovi_Analyses.pdf\|the following lecture]].<br>
	[[Media:FactorAnalysis_jamovi_Lecture.pdf]]<br>
			All analyses described above are used to explore manifest variables, i.e. variables that we can directly observe (and analyse). However, there are also [https://en.wikipedia.org/wiki/Latent_and_observable_variables latent variables] that are inferred indirectly through a mathematical model from observable variables. A typical example for such latent variables are personality dimensions, where we measure characteristics of that personality dimension with several items and afterwards combine them (e.g., by summing them up). A method to find or extract such dimensions based upon the pattern of correlations among questionnaire items is called (exploratory) factor analysis. The [[Media:FactorAnalysis_jamovi_Lecture.pdf\|following lecture]] first describes the principles and inner workings of factor analysis (FA) before turning to how different factor-analytical methods (exploratory FA, principal component analysis, confirmatory FA and reliability analysis) are carried out in jamovi.<br>

	For a couple of years ago, we have changed from SPSS to jamovi for teaching. [https://www.jamovi.org/ jamovi] is a statistics package that is based upon R and quite similar in functionality to SPSS. For further information, please consult the [https://jamovi.readthedocs.io jamovi-documentation] which is available in several languages such as [https://jamovi.readthedocs.io/en/latest/ English], [https://jamovi.readthedocs.io/de/latest/ German], and [https://jamovi.readthedocs.io/no/latest/ Norwegian].<br>		For a couple of years ago, we have changed from SPSS to jamovi for teaching. [https://www.jamovi.org/ jamovi] is a statistics package that is based upon R and quite similar in functionality to SPSS. For further information, please consult the [https://jamovi.readthedocs.io jamovi-documentation] which is available in several languages such as [https://jamovi.readthedocs.io/en/latest/ English], [https://jamovi.readthedocs.io/de/latest/ German], and [https://jamovi.readthedocs.io/no/latest/ Norwegian].<br>

	~~TBA:~~ jamovi ~~and~~ R		It is not only that jamovi implements its analyses as R-code under the hood, it also offers plenty of opportunity to combine the “safer” option of assembling analyses using a graphical user interface (for standard analyses) with the much broader range of analyses available in R. The [[Media:JamoviToR.pdf\| following lecture]] first gives a brief introduction into R (what is it about, variable types, etc.) before demonstrating how to use R from within jamovi (using the [Rj editor]) and how to use jamovi within R (jamovi can output the syntax for the analyses conducted and all analyses are available as [https://cran.r-project.org/package=jmv jmv] R-package, and the R-packages [https://cran.r-project.org/package=jmvconnect jmvconnect] and [https://cran.r-project.org/package=jmvReadWrite jmvReadWrite] help with handling datasets opened in jamovi and jamovi-files). The lecture ends with presenting two typical use cases: using analysis results (e.g., regression coefficients) and importing text data.
	[[Media:JamoviToR.pdf]]

	===Qualitative or mixed-method analyses===		===Qualitative or mixed-method analyses===

Sje025: /* Evaluation methods */

2023-02-07T10:21:07Z

Evaluation methods

← Older revision		Revision as of 10:21, 7 February 2023
Line 65:		Line 65:

	TBA: an introduction into general statistical concepts		TBA: an introduction into general statistical concepts
	[[~~File~~:CrashCourseRefresher_Concepts.pdf]]		[[Media:CrashCourseRefresher_Concepts.pdf]]

	When choosing your evaluation method, a key criterion is whether your variables (predictor/independent and outcome/dependent) are categorical or continuous. Most analysis methods are parametric statistics (i.e., they rely on the assumption that the data are drawn from a distribution, e.g., a standard normal distribution) and based upon the [https://en.wikipedia.org/wiki/General_linear_model General linear model].		When choosing your evaluation method, a key criterion is whether your variables (predictor/independent and outcome/dependent) are categorical or continuous. Most analysis methods are parametric statistics (i.e., they rely on the assumption that the data are drawn from a distribution, e.g., a standard normal distribution) and based upon the [https://en.wikipedia.org/wiki/General_linear_model General linear model].

	TBA: Uni- and bivariate analyses		TBA: Uni- and bivariate analyses
	[[~~File~~:CrashCourseRefresher_jamovi_Analyses.pdf]]		[[Media:CrashCourseRefresher_jamovi_Analyses.pdf]]
	To explore the relationship between categorical predictor and categorical outcome variables, we use frequency analyses.		To explore the relationship between categorical predictor and categorical outcome variables, we use frequency analyses.

Line 76:		Line 76:
	To explore the relationship between categorical predictor and continuous outcome variables, we use [[t-test and Analysis of Variance (ANOVA)]]. It is (in an ANCOVA) also possible to include continuous predictor variables, however the main focus in those analyses is typically on the categorical predictors as those represent the experimentally manipulated variables (e.g., treatment vs. control group).<br>		To explore the relationship between categorical predictor and continuous outcome variables, we use [[t-test and Analysis of Variance (ANOVA)]]. It is (in an ANCOVA) also possible to include continuous predictor variables, however the main focus in those analyses is typically on the categorical predictors as those represent the experimentally manipulated variables (e.g., treatment vs. control group).<br>
	TBA: Factor analysis		TBA: Factor analysis
	[[~~:File~~:FactorAnalysis_jamovi_Lecture.pdf]]<br>		[[Media:FactorAnalysis_jamovi_Lecture.pdf]]<br>

	For a couple of years ago, we have changed from SPSS to jamovi for teaching. [https://www.jamovi.org/ jamovi] is a statistics package that is based upon R and quite similar in functionality to SPSS. For further information, please consult the [https://jamovi.readthedocs.io jamovi-documentation] which is available in several languages such as [https://jamovi.readthedocs.io/en/latest/ English], [https://jamovi.readthedocs.io/de/latest/ German], and [https://jamovi.readthedocs.io/no/latest/ Norwegian].<br>		For a couple of years ago, we have changed from SPSS to jamovi for teaching. [https://www.jamovi.org/ jamovi] is a statistics package that is based upon R and quite similar in functionality to SPSS. For further information, please consult the [https://jamovi.readthedocs.io jamovi-documentation] which is available in several languages such as [https://jamovi.readthedocs.io/en/latest/ English], [https://jamovi.readthedocs.io/de/latest/ German], and [https://jamovi.readthedocs.io/no/latest/ Norwegian].<br>

	TBA: jamovi and R		TBA: jamovi and R
	[[~~:File~~:JamoviToR.pdf]]		[[Media:JamoviToR.pdf]]

	===Qualitative or mixed-method analyses===		===Qualitative or mixed-method analyses===

Sje025: /* Evaluation methods */

2023-02-07T10:20:23Z

Evaluation methods

← Older revision		Revision as of 10:20, 7 February 2023
Line 60:		Line 60:
	==Evaluation methods==		==Evaluation methods==
	===Extracting data===		===Extracting data===
	This [[~~File~~:Shell-Programming.pdf\|presentation]] provides an overview how shell scripts can be used to extract data from log files. The commands in the presentation can be tested using these [[Media:Shell_Materials.zip\|example data]].		This [[Media:Shell-Programming.pdf\|presentation]] provides an overview how shell scripts can be used to extract data from log files. The commands in the presentation can be tested using these [[Media:Shell_Materials.zip\|example data]].

	===Quantitative data analyses===		===Quantitative data analyses===

Sje025: /* Evaluation methods */

2023-02-07T10:20:10Z

Evaluation methods

← Older revision		Revision as of 10:20, 7 February 2023
Line 60:		Line 60:
	==Evaluation methods==		==Evaluation methods==
	===Extracting data===		===Extracting data===
	This [[~~Media~~:Shell-Programming.pdf\|presentation]] provides an overview how shell scripts can be used to extract data from log files. The commands in the presentation can be tested using these [[Media:Shell_Materials.zip\|example data]].		This [[File:Shell-Programming.pdf\|presentation]] provides an overview how shell scripts can be used to extract data from log files. The commands in the presentation can be tested using these [[Media:Shell_Materials.zip\|example data]].

	===Quantitative data analyses===		===Quantitative data analyses===

	TBA: an introduction into general statistical concepts		TBA: an introduction into general statistical concepts
	[[:File:CrashCourseRefresher_Concepts.pdf]]		[[File:CrashCourseRefresher_Concepts.pdf]]

	When choosing your evaluation method, a key criterion is whether your variables (predictor/independent and outcome/dependent) are categorical or continuous. Most analysis methods are parametric statistics (i.e., they rely on the assumption that the data are drawn from a distribution, e.g., a standard normal distribution) and based upon the [https://en.wikipedia.org/wiki/General_linear_model General linear model].		When choosing your evaluation method, a key criterion is whether your variables (predictor/independent and outcome/dependent) are categorical or continuous. Most analysis methods are parametric statistics (i.e., they rely on the assumption that the data are drawn from a distribution, e.g., a standard normal distribution) and based upon the [https://en.wikipedia.org/wiki/General_linear_model General linear model].

	TBA: Uni- and bivariate analyses		TBA: Uni- and bivariate analyses
	[[:File:CrashCourseRefresher_jamovi_Analyses.pdf]]		[[File:CrashCourseRefresher_jamovi_Analyses.pdf]]
	To explore the relationship between categorical predictor and categorical outcome variables, we use frequency analyses.		To explore the relationship between categorical predictor and categorical outcome variables, we use frequency analyses.

Sje025: /* Experiments */

2023-02-06T18:09:23Z

Experiments

← Older revision		Revision as of 18:09, 6 February 2023
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	==Experiments==		==Experiments==
	[[e-prime]]<br>		[[e-prime]]<br>
	[[~~Media:PsychoPy.pdf\|~~PsychoPy]]<br>		[[PsychoPy]]<br>
	[[Web experiments]]<br>		[[Web experiments]]<br>